The role of radiomics in predicting the response to neoadjuvant chemotherapy for breast cancer.

Abstract

Breast cancer exhibits profound biological and spatial heterogeneity, which contributes to variable responses to neoadjuvant chemotherapy (NAC) and challenges precision treatment planning. Radiomics, an emerging discipline that converts standard medical images into high-dimensional quantitative data, offers a non-invasive and reproducible means to capture tumor phenotype, heterogeneity, and treatment-induced changes. This review provides a comprehensive overview of recent advances in radiomics for breast cancer NAC, emphasizing the roles in predicting a pathologic complete response (pCR), monitoring early therapeutic efficacy, and quantifying intratumoral heterogeneity. Among imaging modalities, magnetic resonance imaging (MRI)-based radiomics, particularly utilizing dynamic contrast-enhanced and diffusion-weighted sequences, demonstrates robust predictive performance for the pCR, with multi-center studies reporting area under the curve (AUC) values >0.80. Longitudinal and delta-radiomics approaches further enhance early response evaluation by tracking temporal alterations in imaging features that precede measurable morphologic regression. Radiomic assessment of tumor heterogeneity, especially in triple-negative breast cancer (TNBC), reveals strong associations with immune infiltration, metabolic reprogramming, and therapeutic resistance, providing mechanistic insight into radiomic biomarkers. Integrative multi-omics frameworks, combining radiomics with genomics, transcriptomics and pathomics, are increasingly elucidating the biological underpinnings of imaging phenotypes, improving both model interpretability and clinical relevance. Despite these advances, widespread clinical adoption of radiomics is limited by methodologic variability, lack of standardization, and insufficient external validation. Future efforts should focus on harmonized imaging protocols, explainable artificial intelligence, and prospective multi-center trials to translate radiomics into a clinically actionable tool. Collectively, radiomics represents a transformative approach for individualized response prediction and dynamic treatment optimization in precision breast cancer management (Figure 1).